{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Top title\n",
    "\n",
    "The notebook headers will be printed during testing whenever the verbosity level `nbtest(...; verbose=3)` is greater than or equal to the number of `#` of the header. By default, all headers are printed (`verbose=5`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[34mINFO: Recompiling stale cache file /Users/cedric/.julia/lib/v0.5/NBTesting.ji for module NBTesting.\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "using NBTesting\n",
    "using Base.Test\n",
    "\n",
    "using PyPlot\n",
    "\n",
    "# This defines \n",
    "#     plot(args...; kwargs...) = nothing\n",
    "# but only during testing.\n",
    "@testing_noop plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic Arithmetic\n",
    "\n",
    "The last test fails, but because it's after `#NBSKIP`, it won't be executed during `nbtest`. `#NBSKIP` is also useful for skipping long computations that we don't want to test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[31mTest Failed\n",
      "\u001b[0m  Expression: 7 - 7 == 3\n",
      "   Evaluated: 0 == 3\n"
     ]
    },
    {
     "ename": "LoadError",
     "evalue": "There was an error during testing",
     "output_type": "error",
     "traceback": [
      "There was an error during testing",
      "",
      " in record(::Base.Test.FallbackTestSet, ::Base.Test.Fail) at ./test.jl:397",
      " in do_test(::Base.Test.Returned, ::Expr) at ./test.jl:281"
     ]
    }
   ],
   "source": [
    "x = 10 / 2\n",
    "@test x+x == 10\n",
    "@test 6*10 == 60\n",
    "#NBSKIP\n",
    "@test 7-7 == 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "During normal notebook execution, `is_testing == false`, but it will be `true` during testing. Using this variable, we might test our code using parameters that make it run faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "503203.4224128609"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N = is_testing ? 5 : 100\n",
    "\n",
    "srand(1)\n",
    "M1 = ones(N, N)\n",
    "M2 = rand(N, N);\n",
    "\n",
    "if is_testing\n",
    "    @test 30 < sum(M1 * M2) < 100\n",
    "end\n",
    "\n",
    "sum(M1 * M2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Some plot\n",
    "\n",
    "We could put a `#NBSKIP` before every plotting call, but the `@testing_noop` macro (see above) is more convenient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x3203a5810>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot([1,2,1]);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 0.5.0",
   "language": "julia",
   "name": "julia-0.5"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "0.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
